#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use 
# under the terms of the LICENSE.md file.
#
# For inquiries contact  george.drettakis@inria.fr
#

import os
import json
import random

# import the customized modules
from .arguments import ModelParams
from .gaussian_model import GaussianModel
from .dataset_readers import readObjectInfo
from .utils.system_utils import searchForMaxIteration
from .utils.camera_utils import cameraList_from_camInfos, camera_to_JSON


class Scene:

    gaussians : GaussianModel

    def __init__(self, args : ModelParams, gaussians : GaussianModel, load_iteration=None, shuffle=True, resolution_scales=[1.0]):
        """b
        :param path: Path to colmap scene main folder.
        """
        self.model_path = args.model_path
        self.loaded_iter = None
        self.gaussians = gaussians

        if load_iteration:
            if load_iteration == -1:
                self.loaded_iter = searchForMaxIteration(os.path.join(self.model_path, "point_cloud"))
            else:
                self.loaded_iter = load_iteration
            print("Loading trained model at iteration {}".format(self.loaded_iter))

        self.train_cameras = {}
        self.test_cameras = {}

        scene_info = readObjectInfo(train_dataset=args.referloader, 
                                    test_dataset=args.queryloader,
                                    model_path=args.model_path,
                                    zoom_scale=args.zoom_scale, margin=args.margin, 
                                    random_points3D=args.random_points3D,
                                    num_points=args.num_points, 
                                    )

        # if os.path.exists(os.path.join(args.source_path, "sparse")):
        #     scene_info = sceneLoadTypeCallbacks["Colmap"](args.source_path, args.images, args.eval)
        # elif os.path.exists(os.path.join(args.source_path, "transforms_train.json")):
        #     print("Found transforms_train.json file, assuming Blender data set!")
        #     scene_info = sceneLoadTypeCallbacks["Blender"](args.source_path, args.white_background, args.eval)
        # else:
        #     assert False, "Could not recognize scene type!"


        # if not self.loaded_iter:
        #     with open(scene_info.ply_path, 'rb') as src_file, open(os.path.join(self.model_path, "input.ply") , 'wb') as dest_file:
        #         dest_file.write(src_file.read())
        #     json_cams = []
        #     camlist = []
        #     if scene_info.test_cameras:
        #         camlist.extend(scene_info.test_cameras)
        #     if scene_info.train_cameras:
        #         camlist.extend(scene_info.train_cameras)
        #     for id, cam in enumerate(camlist):
        #         json_cams.append(camera_to_JSON(id, cam))
        #     with open(os.path.join(self.model_path, "cameras.json"), 'w') as file:
        #         json.dump(json_cams, file)

        if shuffle:
            random.shuffle(scene_info.train_cameras)  # Multi-res consistent random shuffling
            random.shuffle(scene_info.test_cameras)  # Multi-res consistent random shuffling

        self.cameras_extent = scene_info.nerf_normalization["radius"]

        for resolution_scale in resolution_scales:
            print("Loading Training Cameras")
            self.train_cameras[resolution_scale] = cameraList_from_camInfos(scene_info.train_cameras, resolution_scale, args)
            print("Loading Test Cameras")
            self.test_cameras[resolution_scale] = cameraList_from_camInfos(scene_info.test_cameras, resolution_scale, args)

        if self.loaded_iter:
            self.gaussians.load_ply(os.path.join(self.model_path,
                                                           "point_cloud",
                                                           "iteration_" + str(self.loaded_iter),
                                                           "point_cloud.ply"))
        else:
            self.gaussians.create_from_pcd(scene_info.point_cloud, self.cameras_extent)

    def save(self, iteration):
        # point_cloud_path = os.path.join(self.model_path, "point_cloud/iteration_{}".format(iteration))
        self.gaussians.save_ply(os.path.join(self.model_path, "3DGO_model.ply"))

    def getTrainCameras(self, scale=1.0):
        return self.train_cameras[scale]

    def getTestCameras(self, scale=1.0):
        return self.test_cameras[scale]